Alternative clouds are booming as companies seek cheaper access to GPUs
Alternative GPU cloud providers such as CoreWeave, Lambda, and others are gaining traction as cheaper, more flexible options for AI workloads compared to AWS, Azure, and GCP. Commenters highlight Nvidia’s incentive to diversify its customer base beyond hyperscalers, the lack of meaningful software lock-in for pure GPU compute, and the appeal of simpler, more predictable pricing versus the big clouds’ complex, often opaque cost structures. At the same time, they note trade-offs around networking, data egress, reliability, and long-term economics, with some predicting a broader shift away from hyperscalers for both GPU training and traditional server workloads.
GPU supply and Nvidia’s incentives
- Commenters argue Nvidia deliberately allocates GPUs to “alt clouds” rather than only hyperscalers to avoid dependence on a few giants that are building their own chips.
- Diversified customers reduce monopsony risk and preserve demand if major clouds shift to in‑house silicon (TPU, Graviton, Azure chips).
- Some note close relationships between Nvidia and certain alternative providers, implying not all are at arm’s length.
AMD vs. Nvidia ecosystem
- Multiple posts say AMD’s MI250/MI300 hardware is competitive, but lack of easy, cheap cloud access and weaker tooling keeps the ecosystem Nvidia‑centric.
- CUDA and Nvidia’s long-term software investment are seen as the real moat; AMD historically focused on gaming and lacked capital for multiple “moonshots,” but is now course‑correcting.
- Skepticism remains about AMD’s ability to match Nvidia’s drivers and software stack.
Cloud pricing, margins, and “confusing bills”
- Many criticize big-cloud pricing (CPU, SSD/SAN, egress, EFS, etc.) as opaque and expensive, with surprise bills common.
- Some see AWS/GCP/Azure as a ZIRP-era phenomenon now being unwound; others counter that cloud usage and revenues are still growing and that costs can be managed with expertise.
- There is debate whether “cloud vs. on‑prem” savings hold once you include staff and HA needs; networking, redundancy, and compliance are cited as major hidden costs.
Alternative GPU clouds: promise and caveats
- Alt providers often offer significantly cheaper GPU hours than hyperscalers and fewer hoops to get quota.
- Some services are praised for ease of use (simple signup, fast access, autoscaling to zero).
- Others are criticized as “predatory” when capacity is scarce or tied to long-term contracts; defenders say this reflects marketplace dynamics and high demand.
- Specific price examples (A100/H100) spark debate over whether offerings are subsidized by VC versus sustainable cross‑subsidy across SKUs.
Lock-in, tooling, and multi-cloud
- Pure GPU IaaS is seen as less sticky than full AWS-style platforms; lock‑in mostly comes from data gravity and proprietary software layers.
- Some inference platforms add abstractions that ease use but increase lock‑in, which turns off teams already invested in Triton or custom stacks.
- New tools (e.g., multi-cloud schedulers, “single consoles” over many GPU clouds) aim to route workloads to the cheapest/available GPUs and mitigate lock‑in.
On‑prem, colo, and dedicated servers
- Numerous commenters report large savings using dedicated servers/colo (Hetzner, OVH, others) versus big cloud, especially for bandwidth-heavy or steady workloads.
- Others emphasize operational complexity, HA, and staffing as reasons many companies still prefer managed clouds, at least early on.
Macro outlook: boom or bubble
- Some predict current GPU capex and alt-cloud boom will “crash big time” as AI adoption metrics disappoint and costs fall.
- Others argue demand for powerful compute is structurally durable, even if today’s business models evolve.